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Informal influence in the Asian Development Bank

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Abstract

Through case studies and empirical analysis, scholars have uncovered convincing evidence that individual donors influence lending decisions of international financial institutions (IFIs) such as the World Bank and the Asian Development Bank. Less clear are the mechanisms by which donors exert influence. Potential mechanisms are either formal or informal. Formal influence is through official decisions of the board of executive directors while informal influence covers all other channels. This paper explores the role of informal influence at the Asian Development Bank by examining the flow of funds after loans are approved. Controlling for commitments (loan approvals), are subsequent disbursements linked to the interests of the key shareholders, Japan and the U.S.? I compare these findings with results for the World Bank and consider implications for institutional reforms.

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Notes

  1. Although the subscript i is redundant given that j indexes all projects (across all countries and time periods), it is helpful for tracking other variables.

  2. This allows for loans that disburse over several years rather than assuming immediate disbursement as in Bulíř and Hamann (2003, 2007), Celasun and Walliser (2008), and Odedokun (2003).

  3. PLAID includes some project-level disbursement data but these are cumulative, not annual.

  4. More precisely, these are factors that: 1) may influence the disbursement rate; and, 2) are consistent with the ADB’s apolitical charter.

  5. PLAID data on ADB projects also lack closing dates.

  6. Using military aid as a proxy for geopolitical importance would be reasonable for the U.S. but not for Japan. U.S. military aid proves insignificant if included in the specifications reported below.

  7. Although many votes are not close, a vote buying model is still relevant if the U.S. values support regardless of the outcome. For the UNSC, Dreher et al. (2009b) point out that the U.S. works toward consensus by rewarding UNSC members for their votes even when those votes are not required (for example, “No” votes where the U.S. could simply exercise its veto).

  8. It is also possible that there would be a non-political shift in the supply side of World Bank funding if the country also adopts reforms more in-line with prevailing World Bank policy prescriptions.

  9. Indeed, it could decrease since alignment is bounded from above at unity.

  10. Including a separate fixed effect for every government poses problems in terms of degrees of freedom. Also, exploratory regressions suggest only substantial changes matter: there is a significant drop in funding levels the year before a substantial change in government while the effect is much smaller and not significant if all changes in government are included.

  11. Of course, Wittkopf did not have the benefit of the State Department’s post-1982 designations. Indeed, an increased emphasis in U.S. Congress in early 1980s to identify what the public was getting for its aid tax dollars resulted in the law mandating State Department reporting.

  12. See Fleck and Kilby (2006) for a discussion of using like-minded donor aid as a control variable. I omit Canada from the like-minded donor group as it is already included in the G7-2.

  13. Because exports and imports are highly correlated, it can be difficult to interpret the coefficient on individual components of trade.

  14. The sample excludes eight influential observations (Afghanistan 1992; Solomon Islands 1995, 2001; Tuvalu 2003–5; Vanuatu 2003; Vietnam 1985). Excluding these data points, results are robust across specifications and sub-samples (e.g., omitting individual countries or years). Due to the relatively small number of borrowing member countries in the ADB and the extremes represented (in terms of size, commercial importance, and geopolitical significance), the problem of influential outliers and how to delineate the estimation sample is particularly thorny in this analysis (see the discussion of China below). Appendix B lists all countries included in the analysis.

  15. I use older IDS CD-ROM data to fill-in missing values in new data to recover countries dropped from OECD coverage (especially from 2007 on). IDS data are ahistorical in the sense that the DAC alters historical data to fit current national boundaries. When two countries unite, their individual time series are combined so that current and historical data are available only in the combined format. When a country splinters, the DAC divides its data accordingly, again even back through the period when the country was united. When a country drops from DAC coverage (e.g., in 2007 when CEECs/NICs were dropped as no longer “developing”), the historical data for the country vanish. To avoid dropping additional observations due to log of zero when including bilateral aid variables in the specification, I add a small constant (0.01, i.e., $10,000) prior to taking logs. Results are similar if I avoid this step by dropping these observations or using the bilateral aid to GDP ratio instead.

  16. Only low interest loans from the ADF and grants qualify as ODA and appear in the DAC commitment data.

  17. I collected State Department data at the vote level rather than aggregated to the country level so that measures can be constructed for Japan and the other G7 countries.

  18. Because the ADB Projects Database does not indicate the source of funds (OCR versus ADF), I use PLAID data to construct this variable. Also using PLAID data for Original Commitments yields very similar results; the correlation between Original Commitments derived from the two sources is greater than 0.99 though the PLAID data appear to omit a large loan for Korea in 1997.

  19. G7-2 aid and Like-minded donor aid are averages over their groups. When these variables are converted to logs, I include the average of the log (where defined) rather than the log of the average. Results are generally not sensitive to how this step is done. The same applies to trade variables below. This approach makes more sense when including the common agency measures later in the paper.

  20. One might think of trade flows scaled by donor GDP to capture the importance of a trading partner. Given the log specification used below, this scaling factor just folds into the year dummies included in all specifications. An alternative approach is to examine aid shares as in Kilby (2006).

  21. See Cameron and Trivedi (2005) for a discussion of these different approaches.

  22. I rely on two types of nonlinearities, those arising from the probit function and using dichotomous versions of bilateral aid variables in the selection equation but continuous versions of these same variables in the allocation equation. In addition, I had to progressively strip out variables (e.g., year dummies) to achieve convergence.

  23. It is a possible to estimate a selection equation without year dummies (e.g., with a trend term) but this severely limits comparisons with the allocation equation and hence interpretation of results. The underlying source of this estimation problem is limited variability in the ADB sample.

  24. As noted above, I only generate a new government fixed effect if the new government differs substantially (|Δpolity|>3). In the estimation sample with 33 countries, there are 49 separately identified governments. Results are generally similar with country fixed effects.

  25. A one standard deviation increase in the average of the log of G7-2 aid corresponds to a $20 million increase in the level of average G7-2 aid, i.e., $100 million increase counting all five donors. If Vietnam is dropped from the sample, U.S. bilateral aid becomes marginally significant.

  26. This assumes formal donor influence does not substantially bias the mix of projects toward those with faster disbursement profiles.

  27. For example, using a different sample and a specification that does not include fixed effects, Kilby (2006) does find evidence of Japanese influence in ADB disbursements.

  28. I identified China as an outlier through a robustness check that sequentially omits entire countries or entire years from the estimation sample. Only the regression omitting China yields a significant coefficient on Japan other votes. Two factors contribute to this. First, disbursements to China tend to be large, making Chinese observations potentially influential. Second, China’s size and important role in geopolitics (e.g., the only ADB regional member with a permanent seat on the UNSC) makes the notion of buying its UN vote with ADB funding far less plausible.

  29. Note that the samples (even when restricted) are not identical.

  30. Copelovitch’s measure of variation is the coefficient of variation (COV—standard deviation divided by the mean after the data have been shifted right so that all values are positive). I use the standard deviation (STD) as my results were sensitive to the size of the rightward shift needed for the COV calculation.

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Acknowledgements

I thank the organizers of and participants in the Egon Sohmen Memorial Conference on the Political Economy of International Financial Institutions in Tübingen, Germany, June 2010, especially Peter Bernholz, Axel Dreher, and Roland Vaubel. I am also grateful for invaluable feedback during the Political Science Department Seminar at BYU and the Georgetown University International Theory and Research Seminar, particularly comments from Daniel Lim, Daniel Nielson, and James Vreeland. Finally, I thank two anonymous referees for a careful reading of my work.

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  1. Department of Economics, Villanova University, Villanova, PA, USA

    Christopher Kilby

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Appendices

Appendix A: UN alignment measure

Basic Notation:

a :

= country index

= i for recipient country

= d for donor country

k :

= index of UN roll call votes

t :

= year

M t :

= {important UNGA roll call votes in year t}

N t :

= {other UNGA roll call votes on regular session resolutions that passed in year t}

Define the possible votes cast by country a in roll call vote k in year t as

v akt :

= −1 if a votes “No”

= 0 if a abstains or is absent

= 1 if a votes “Yes”

The voting alignment score of donor d and recipient i on UN roll call vote k in year t is

ρ dikt :

= 1 if v dkt =v ikt

= 0 if v dkt =−v ikt and v dkt ≠ 0

= .5 otherwise

This voting alignment score has the desirable property that ρ aakt  = 1 under all circumstances. The overall voting alignment on important UN votes for donor d and recipient i in year t is the average over the applicable votes for the year:

$$ \rho_{dit}^M = \frac{1}{{\left| {{M_t}} \right|}}\mathop \sum \limits_{k \in {M_t}} {\rho_{dikt}} $$
(A1)

The overall voting alignment on other UN votes for donor d and recipient i in year t is the average over the applicable votes for the year:

$$ \rho_{dit}^N = \frac{1}{{\left| {{N_t}} \right|}}\mathop \sum \limits_{k \in {N_t}} {\rho_{dikt}} $$
(A2)

The voting alignment measure used in the empirical analysis for donor d and recipient i in year t is

$$ diffd_{{it}} = \rho ^{M}_{{dit}} - \rho ^{{\text{N}}}_{{{\text{dit}}}} $$
(A3)

When the alignment measure is for a group of donor countries, it is the unweighted average of the measures for the member countries.

Appendix B: Countries in estimation sample

ADB borrowers (current and former) included in estimation sample:Afghanistan MyanmarAzerbaijan NepalBangladesh PakistanBhutan Papua New GuineaCambodia PhilippinesChina SamoaFiji SingaporeIndia Solomon IslandsIndonesia Sri LankaKazakstan TajikistanKiribati ThailandKorea TongaKyrgyz Republic TuvaluLaos UzbekistanMalaysia VanuatuMaldives Viet NamMongolia Myanmar

ADB borrowers (current and former) dropped from estimation sample due to data constraints:Armenia Micronesia, Fed.StatesChinese Taipei NauruCook Islands PalauGeorgia Timor-LesteHong Kong, China TurkmenistanMarshall Islands

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Kilby, C. Informal influence in the Asian Development Bank. Rev Int Organ 6, 223–257 (2011). https://doi.org/10.1007/s11558-011-9110-0

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